FastInstanceSegmentationRegressionLoss#

class FastInstanceSegmentationRegressionLoss(cost_mask: float = 1.0, cost_dice: float = 1.0, cost_class: float = 0.0, num_points: int = 0, ignore_index: int = -1, loss_weight_focal: float = 1.0, loss_weight_dice: float = 1.0, cls_weight_matched: float = 2.0, cls_weight_noobj: float = 0.1, focal_alpha: float = 0.25, focal_gamma: float = 2.0, truth_label: str = 'segment', aux_loss_weight: float = 1.0, regression_targets=None)[source]#

Bases: Module

Instance segmentation loss plus configurable per-instance regressions.

Extends FastInstanceSegmentationLoss (same Hungarian matching, focal mask, Dice, and classification terms) with an arbitrary set of continuous regression heads supervised on matched query/instance pairs. Each entry in regression_targets names a prediction key (default pred_<name>), an input_dict target key (default <name>), a per-instance aggregation ("mean" or "first"), a per-target criterion (default SmoothL1RegressionLoss), and a loss_weight. Deep supervision over aux_outputs is summed and scaled by aux_loss_weight.

forward(pred, input_dict) returns (loss, components) with the mask/ class diagnostics plus one scalar per regression target. Registered as FastInstanceSegmentationRegressionLoss.

Parameters:

  • cost_mask (float) – Matcher weight on the focal mask cost. Defaults to 1.0.

  • cost_dice (float) – Matcher weight on the Dice cost. Defaults to 1.0.

  • cost_class (float) – Matcher weight on the classification cost. Defaults to 0.0.

  • num_points (int) – Sampled points for the mask cost (0 uses all). Defaults to 0.

  • ignore_index (int) – Instance/segment id treated as void. Defaults to -1.

  • loss_weight_focal (float) – Weight on the focal mask loss. Defaults to 1.0.

  • loss_weight_dice (float) – Weight on the Dice loss. Defaults to 1.0.

  • cls_weight_matched (float) – Weight on the matched-query CE term. Defaults to 2.0.

  • cls_weight_noobj (float) – Weight on the no-object CE term. Defaults to 0.1.

  • focal_alpha (float) – Focal-loss positive-class weight. Defaults to 0.25.

  • focal_gamma (float) – Focal-loss focusing exponent. Defaults to 2.0.

  • truth_label (str) – input_dict key holding per-point instance ids. Defaults to "segment".

  • aux_loss_weight (float) – Scale on the summed auxiliary loss. Defaults to 1.0.

  • regression_targets – List (or name-keyed dict) of regression-target configs as described above. Defaults to None.

Example

>>> import torch
>>> from pimm.models.losses.instance_regression_fast import (
...     FastInstanceSegmentationRegressionLoss)
>>> crit = FastInstanceSegmentationRegressionLoss(
...     truth_label="instance",
...     regression_targets=[dict(name="momentum", loss_weight=1.0)])
>>> # pred needs pred_masks plus pred_<name> for each regression target
>>> pred = {"pred_masks": [torch.randn(8, 50)],
...         "pred_momentum": [torch.randn(8, 1)]}
>>> inst = torch.zeros(50, dtype=torch.long)
>>> inst[10:30] = 1; inst[30:50] = 2
>>> input_dict = {"instance": inst.view(-1, 1), "momentum": torch.randn(50, 1)}
>>> loss, comp = crit(pred, input_dict)   # scalar loss + diagnostics dict
>>> "momentum" in comp                    # one scalar per regression target
True
FastInstanceSegmentationRegressionLoss.forward(pred: Dict, input_dict: Dict) → Tensor[source]#